Introduction

• Air pollution can be detrimental to both our health and the climate
  • Outdoor and indoor air pollution cause chronic pain, respiratory diseases, shortened lifespan
  • Air pollution kills about 7 million people worldwide every year
  • Hopefully this information can showcase the importance of air pollution and that we should be more mindful about our planet
• Overview
  • We will see how different air pollution types affect the population
  • compare past and present population numbers
  • determine which air pollutant type has the highest associated death rate

Packages Required

#This will allow us to filter through our data 
library(tidyverse)
library(dplyr)
#This will help us plot figures to showcase our findings
library(ggplot2)
#This will help us organize and display our data as necessary 
library(knitr)
library(kableExtra)
#This expands our plot uses 
library(plotly)
#Scientific Notation Disabled 
options(scipen=999)

Deaths Data

• Deaths from pollution:
  • Kaggle: [https://www.kaggle.com/datasets/akshat0giri/death-due-to-air-pollution-19902017]
  • Author: Akshat Giri
  • Last update: 2 years ago

Import the deaths-due-to-air-pollution data

deaths_df <- data.frame(read.csv("death-rates-from-air-pollution.csv"))
glimpse(deaths_df)

## Rows: 6,468
## Columns: 7
## $ Entity                                          <chr> "Afghanistan", "Afghan…
## $ Code                                            <chr> "AFG", "AFG", "AFG", "…
## $ Year                                            <int> 1990, 1991, 1992, 1993…
## $ Air.pollution..total...deaths.per.100.000.      <dbl> 299.4773, 291.2780, 27…
## $ Indoor.air.pollution..deaths.per.100.000.       <dbl> 250.3629, 242.5751, 23…
## $ Outdoor.particulate.matter..deaths.per.100.000. <dbl> 46.44659, 46.03384, 44…
## $ Outdoor.ozone.pollution..deaths.per.100.000.    <dbl> 5.616442, 5.603960, 5.…

We are going to rename a few of the columns and glimpse the data

colnames(deaths_df) <- c("country", "acronym", "year", "total_deaths", "indoor_deaths", "outdoor_deaths", "ozone_deaths")

glimpse(deaths_df)

## Rows: 6,468
## Columns: 7
## $ country        <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanist…
## $ acronym        <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG",…
## $ year           <int> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1…
## $ total_deaths   <dbl> 299.4773, 291.2780, 278.9631, 278.7908, 287.1629, 288.0…
## $ indoor_deaths  <dbl> 250.3629, 242.5751, 232.0439, 231.6481, 238.8372, 239.9…
## $ outdoor_deaths <dbl> 46.44659, 46.03384, 44.24377, 44.44015, 45.59433, 45.36…
## $ ozone_deaths   <dbl> 5.616442, 5.603960, 5.611822, 5.655266, 5.718922, 5.739…

Data Variables

Variables that interest us here include:

• country
• total_deaths: per 100,000
• indoor_deaths: Indoor air pollution is considered pollution that occurs in the household. Cooking with solid fuels:
  • Wood
  • Crop waste, dung
  • Charcoal, coal
• outdoor_deaths: Outdoor air or ambient air are emissions caused by combustion processes from motor vehicles, solid fuel burning and industry
  • Ozone (O3)
  • Particulate matter (PM10 and PM2.5)
  • Nitrogen dioxide (NO2)
  • Carbon monoxide (CO)
  • Sulfur dioxide (SO2)
• ozone_deaths: Ozone is a gas that occurs both in Earth’s upper atmosphere and at ground level. Ozone in the atmosphere is an important and helpful greenhouse gas, but ground-level ozone is created by extensive use of fossil fuels:
  • Pollutants emitted by cars
  • Power plants, industrial boilers, refineries, chemical plants

World Population Data

• World Population:
  • Kaggle: [https://www.kaggle.com/datasets/imdevskp/world-population-19602018]
  • Author: Devakumar K. P.
  • Last update: 2 Years ago

Now, let’s take a look at the population data.

world_pop <- read.csv("population_total_long.csv")
glimpse(world_pop)

## Rows: 12,595
## Columns: 3
## $ Country.Name <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorra", "…
## $ Year         <int> 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 196…
## $ Count        <int> 54211, 8996973, 5454933, 1608800, 13411, 92418, 20481779,…

To get a general idea of ‘deaths-dataframe’ we made, let’s make a plots to see what’s happening. This is a plot of indoor x outdoor deaths around the world by country.

This is a mess, and so we chose two countries from each continent (a high-population and a low-population country) to graph.

We selected a high population from each continent and used the formula below to determine the low population.

Low population = high population * .10

Combine Data Sets

Death Count

Which country has the highest average death count?

Let’s make a table depicting the high and low populated countries and their respected death count due to pollution.

Here’s a graph to clearly visualize the previous table

So we’ve looked at the deaths due to pollution, but what percentage of the population was affected?

Pollution Types

Which type of pollution has the greatest number of deaths?

Pollution Over Time

Let’s look at the previous two decades and compare the death count Has there been a change?

Let’s graph the previous tables!

The first decade.

This shows the second decade.

By comparing each pollutant type, we can determine which year and country had the highest numbers of deaths

Indoor Deaths

Outdoor Deaths

Ozone Deaths

Which is worse?

outdoor or indoor pollution?

Let’s reintroduce a graph we looked at earlier. Instead this time we will combine the pollutant types together.

We cannot conclude which is worse.

• Low Populated Countries:
  • it’s half-and-half
• High Populated Countries:
  • Outdoor pollution seems to be more detrimental with the exception of two countries in this sample set.

Summary

• Which country has the highest average death count?
  • High Population: Pakistan
  • Low Population: Malawi
• Has the percentage of the affected population decreased or increased over time?
  • Generally it is decreasing for both High and Low populated countries
• Which pollutant type has the greatest number of deaths?
  • High Population: Indoor Pollution
  • Low Population: Indoor Pollution
• How has the death count changed over the past two decades?
  • 1996-2006:
    • High Population: Decreases
    • Low Population: Decreases
  • 2007-2017:
    • High Population: Decreases
    • Low Population: Decreases
• Which year and country had the highest number of deaths per pollutant type?
  • We looked at years 1996-2017
  • Indoor: Pakistan and Malawi were mainly affected in 1996
  • Outdoor: Serbia and Pakistan were the top countries.
    • 2011 was the worst for Pakistan.
    • 2005 was the worst for Serbia
    • Sri Lanka and Tonga increased, but Sri Lanka had a steep decrease after 2015
  • Outdoor Ozone: Pakistan and Malawi were the top countries. 1997 was the worst for Pakistan.
    • United States was the second highest amount of deaths among the higher populated countries
    • Pakistan decreased and then slightly increased
• Which is worse - outdoor or indoor pollution?
  • Inconclusive

Sources

• [https://www.kaggle.com/datasets/akshat0giri/death-due-to-air-pollution-19902017 ]

• [https://www.worldometers.info/population/world/]

• [https://www.who.int/health-topics/air-pollution#tab=tab_1]

• [https://www.epa.gov/ground-level-ozone-pollution/ground-level-ozone-basics]

• [https://www.health.nsw.gov.au/environment/air/Pages/outdoor-air-pollution.aspx]

• [https://www.mana.md/indoor-air-vs-outdoor-air/]

• [https://www.kaggle.com/datasets/imdevskp/world-population-19602018]